SignificanceDo human societies from around the world exhibit similarities in the way that they are structured and show commonalities in the ways that they have evolved? To address these long-standing questions, we constructed a database of historical and archaeological information from 30 regions around the world over the last 10,000 years. Our analyses revealed that characteristics, such as social scale, economy, features of governance, and information systems, show strong evolutionary relationships with each other and that complexity of a society across different world regions can be meaningfully measured using a single principal component of variation. Our findings highlight the power of the sciences and humanities working together to rigorously test hypotheses about general rules that may have shaped human history.

During the Holocene, the scale and complexity of human societies increased markedly. Generations of scholars have proposed different theories explaining this expansion, which range from broadly functionalist explanations, focusing on the provision of public goods, to conflict theories, emphasizing the role of class struggle or warfare. To quantitatively test these theories, we develop a general dynamical model based on the theoretical framework of cultural macroevolution. Using this model and Seshat: Global History Databank, we test 17 potential predictor variables proxying mechanisms suggested by major theories of sociopolitical complexity (and >100,000 combinations of these predictors). The best-supported model indicates a strong causal role played by a combination of increasing agricultural productivity and invention/adoption of military technologies (most notably, iron weapons and cavalry in the first millennium BCE).

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Abstract. This paper describes OWL ontology re-engineering from the wikibased social science codebook (thesaurus) developed by the Seshat: Global History Databank. The ontology describes human history as a set of over 1500 time series variables and supports variable uncertainty, temporal scoping, annotations and bibliographic references. The ontology was developed to transition from traditional social science data collection and storage techniques to an RDF-based approach. RDF supports automated generation of high usability data entry and validation tools, data quality management, incorporation of facts from the web of data and management of the data curation lifecycle.This ontology re-engineering exercise identified several pitfalls in modelling social science codebooks with semantic web technologies; provided insights into the practical application of OWL to complex, real-world modelling challenges; and has enabled the construction of new, RDF-based tools to support the large-scale Seshat data curation effort. The Seshat ontology is an exemplar of a set of ontology design patterns for modelling unncertainty or temporal bounds in standard RDF. Thus the paper provides guidance for deploying RDF in the social sciences. Within Seshat, OWL-based data quality management will assure the data is suitable for statistical analysis. Publication of Seshat as highquality, linked open data will enable other researchers to build on it.

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